Abstract: Evaluations of injuries and the assessment of residual dexterity is carried out by clinicians by means of standardized tests. Several dexterity tests have been developed and all of them require the test administrator to sit in front of the subject and visually assess the motions performed. Automatic tools can reduce the time needed for the evaluation and can provide additional useful information. For many of the available dexterity tests, automated evaluation methods have been developed. However, for the Minnesota dexterity test, despite being widely used, just one automated solution based on multiple depth sensor cameras was developed. This study aims to provide a wearable and flexible method to automatically evaluate the outcomes of Minnesota dexterity tests. The proposed methodology consists of a parametric model that is capable of processing acceleration data collected with an IMU attached to the centre of mass of the subject’s dominant hand. The developed model was successfully validated against a subject study with ten participants.

Keywords: Automatic assessment | Biomechanics | Manual dexterity | Parametric model | Wearable devices

Abstract: As part of the technological transformation of societies, embodied digital technologies (EDTs) such as autonomous service and delivery robots, socially interactive robots, self-driving cars, virtual (service) agents, “intelligent” wearables, and protheses (to name only a few) have become a part of our daily lives and will further proliferate in the future, with wide-ranging implications for human-technology interaction. EDTs are artificial agents that are physically represented in areas previously only occupied by humans. We use the term hybrid societies for future societies in which humans, human-technological hybrids, and EDTs interact and share public spaces (e.g., roads, sidewalks, malls, and public buildings). Specifically, we define a hybrid society as a collective of embodied agents (including humans, partly human actors such as cyborgs, and non-human EDTs such as robots) with the capability to engage in intelligible encounters, who interact and communicate, who can meaningfully reference each other as members of society, and who vary in terms of autonomy, agency, and responsibility. We propose that psychological and technological factors governing effective interactions among humans and EDTs in hybrid societies can be subsumed under two research questions: (1) How do humans perceive and interact with embodied technologies? (2) What are design principles and strategies for designing accountable embodied technology, i.e., embodied technologies that effectively signal to others who encounter them what they can do, want, and how one is supposed to interact with them? We lay out a research agenda for addressing these questions and discuss potential implications.

Keywords: Embodied digital technologies | Hybrid societies | Public spaces | Smooth interaction | Technological transformation

Abstract: Gestures are one of the most intuitive ways that humans use to interact with others or convey information. The idea that hand gestures could facilitate human–machine interaction has recently gained increasing interest among researchers. Various technologies have been investigated, providing both visual and sensor-based gesture recognition. While camera-based solutions suffer from the constraint of specific and expensive laboratories, wearable sensor-based solutions allow lower costs and higher flexibility, enabling gesture recognition even in public spaces. Although several solutions are available in the literature, most of them focus on specific sensor principles and specific gestures. The aim of this work is to recognize basic gesture components, defined as primary elements that compose more complex gestures, using both force myography (FMG) and electromyography (EMG), and to highlight their strengths and weaknesses. This will provide the foundation for the recognition of more complex human upper limb movements. To this end, a laboratory study was conducted with ten participants. FMG signals were collected by means of a wearable sensor network consisting of an instrumented smart band with eight pressure sensors and a wireless datalogger. EMG data were acquired using three commercial sensors. The recorded data were analyzed using k-nearest neighbor classifier and extreme learning machine algorithms. The results showed that the data recorded using FMG had higher accuracy in recognizing the ten different static hand gestures studied compared to the EMG data.

Keywords: Body-Attached sensor networks | EMG | FMG | Gesture recognition | Human–Machine interaction

Abstract: This article presents a post-design methodology for optimizing workpiece placement withinthe predefined workspace of a five degrees-of-freedom parallel kinematic machine tool(PKMT) with SPR–4SPRR architecture. The objective is to identify workpiece locationsthat improve force-transmission characteristics and reduce actuator effort during machining, without modifying machine geometry or control architecture. The approach combinesanalytical inverse kinematics, screw-theory-based Jacobian formulation, and force-manipulability analysis to evaluate the force-transmission capability of the machine across theworkspace. The workspace is discretized into a dense three-dimensional grid and analyzedfor three representative tool tilt angles (0 deg, 15 deg, and 45 deg). A data-driven thresholdbased on the empirical manipulability distribution is used to retain only well-conditionedconfigurations, and the optimal workpiece position is defined as the manipulabilityweighted centroid of the resulting high-performance region. The method is assessed onan industrial-scale model of the METROM pentapod in MATLAB SIMSCAPE through simulatedmachining trajectories under representative quasi-static cutting loads. Results show thatthe optimized placement reduces mean peak actuator loads across additional simulatedpaths by 7.66% at 0 deg, 1.36% at 15 deg, and 21.22% at 45 deg tilt, with correspondingaverage force reductions of 1.66 N, 0.597 N, and 5.08 N. These findings demonstrate thatworkspace-aware workpiece placement can enhance the mechanical operating conditionsof PKMTs and provide a practical post-deployment strategy for improving machining performance.

Keywords: design and process innovation | kinematics | manufacturing processes | mechanism synthesis and analysis | mechanisms and robots | parallel robots | theoretical and computational kinematics

Abstract: This study investigates how children with visual impairments communicate affectively via touch and how they interpret vibrotactile feedback. Since visual stimuli can only be used to a limited extent, touch-based communication plays a central role in perception and social interaction for these children. Two children (aged 14 and 15) engaged with: (1) a Sensor Bear to express emotions through touch, (2) an Actuator Bear to decode vibration patterns, and (3) a Conventional Bear for free exploration and personal preferences. This empathy-centric approach facilitates affective communication through tangible mirroring of affective states between remote partners. Findings reveal diverse touch preferences, the essential role of material softness for affective expression, mixed success in vibration pattern interpretation, and challenges in imagining remote affective scenarios.

Keywords: Accessibility | Affective interaction | Empathy-centered design | Haptic interfaces | Tangible interaction | Vibrotactile communication | Visual impairment

Abstract: Measurements for the functional evaluation of knee joint mobility are essential, e.g., to determine the need for invasive surgery and quantify patient outcomes. While vision-based motion capture systems offer high precision, they require specialized laboratories and trained staff. This paper introduces KARMA (Knee Angle Resistive Measurement Apparatus), an instrumented knee sleeve with six resistive flex sensors to measure knee joint angles. The proposed device features wearability, low cost, and flexibility, enabling pre- and post-operative evaluations for patients eligible for knee surgery. A case study was performed using a marker-based motion capture system as a reference. This demonstrated KARMA’s ability to measure flexion-extension angles but highlighted its inability to measure abduction-adduction and internal-external rotation angles.

Keywords: Flex sensors | Joint angles | Knee arthroplasty | Motion analysis | Wearable sensors

Abstract: Virtual prototyping is a strategic practice in the research and development of innovative products and machine tools. Virtual prototyping allows the integration of multidomain simulations into the designing process to replicate and analyze the impact of design choices on the overall system performance, reducing time-to-market while simultaneously improving quality. The current paper provided a methodological approach to model complex machine tools and perform virtual testing. As a use case for this study, a parallel kinematic machine (Pentapod P800, METROM Mechatronische Maschinen GmbH, Germany) is investigated. The adoption of these complex machine tools within the industrial context and the design of parallel kinematic machines can be eased by the implementation of methodologies capable of reducing efforts and risks during the analysis and the testing phases, prior to actual commissioning. In this scenario, virtual testing guarantees generality, completeness, and quick response. Therefore, the multibody model of the Pentapod P800 was developed following the proposed framework. Then, the simulation of a test trajectory was successfully carried out. The results show that this approach might lead to the design and implementation of a parallel kinematics machine reducing risks, time, and costs.

Keywords: Digital modelling | Multibody modelling | Parallel kinematic machines | Virtual prototyping

Abstract: Performance diagnostics, such as monitoring athletes’ movements, are essential for enhancing their performances. Wearable sensor networks are commonly employed for this purpose. However, designing such networks can be complex and often necessitates expensive and time-consuming pilot studies. To address this, we propose utilizing multibody models and digital sensors to simulate human movements and virtually test the development of sensor setups. In this study, we present an improved multibody model of the human upper limb, building upon an existing virtual environment from the literature for simulating human gestures. Our model incorporates a streamlined representation of the human torso and clavicle, along with their corresponding joints. The primary objective is to introduce a novel actuation method for the updated model, which only requires the positions of a predefined point as input, utilizing inverse kinematics to define the joint parameters. This approach reduces the amount of data required for simulation. To showcase the capabilities of this method, we acquired and utilized the trajectories of the center of gravity of the hand of a handball player executing a set shot to animate the multibody model. During the user study, the reference trajectories were compared to the trajectories obtained for the hand model during the simulation, revealing a strong agreement and affirming the feasibility of the proposed method.

Keywords: Human Movements | Inverse Kinematics | Multibody Modelling | Virtual Environment | Wearable Sensors

Abstract: Humans are able to communicate by a wide variety of means. Gestures often play an important role in this multimodal communication. In order to also ensure robust interaction between humans and machines, it is important that machines are able to recognize human gestures. This typically requires time-consuming subject tests that limit the number of conditions that can be tested. However, by moving these tests from the physical to a virtual environment, each test condition can be evaluated quickly, eliminating the need for numerous repetitions. The purpose of this work was to validate the use of a virtual test environment in comparison to physical testing. This was done by conducting a subject test and developing a virtual model of the human upper limb. The motion profile of the subject performing a simple gesture was recorded with a visual optical motion capture system and used as input for the newly developed virtual model. Acceleration signals captured with an IMU attached to the subject's right wrist were used as a reference signal and compared to signals simulated by a digital twin of the sensor. The pilot study proved the capabilities of the proposed approach and showed some of its limitations.

Keywords: Digital twin | Human body model | Human gestures | Simulation | Virtual environment

Abstract: Objective and reliable assessment of motor functions, such as dexterity, is a key point for evaluating worker’s abilities. In this context, the proposed work presents a tool for objective automatic assessment of the Minnesota Dexterity Test using cameras with depth sensors. Typical performance measurements (i.e., total time and associated percentiles) were estimated using custom algorithms. In addition, the possibility to identify the qualifiers for the code d440 of the International Classification of Functioning, Disability and Health was implemented in the developed algorithms. The proposed tool can also identify the mistakes most frequently committed by the subjects. To prove the capabilities of the proposed method, a series of experimental trials was conducted with 10 healthy young volunteers. Results showed that the developed tool helps clinicians to obtain performance feedback and evaluate patients’ dexterity quickly without bias.

Keywords: Automatic assessment | Biomechanics | Depth cameras | Manual dexterity | Motion capture

Abstract: The analysis of human gait is of fundamental importance for the monitoring and enhancement of athletes’ performances. The kinematics and kinetics of human gait are mostly investigated with optical motion capture systems and force plates that require specialised laboratories and limit the possible test conditions. On the contrary, body-attached sensor networks provide an opportunity for long-term acquisitions in unsupervised, naturalistic scenarios. In this study, a wearable sensor network consisting of two wireless dataloggers and two instrumented insoles with eight pressure sensors each is used. Custom algorithms for the automatic detection of hike events and the estimation of the related temporal parameters based on sensors data are presented. The proposed algorithms were tested against laboratory measurements performed on an instrumented treadmill and showed relative errors of less than 2.5% in the estimation of stride time, step time and cadence. Higher relative errors were found in the estimation of stance and swing phases. The developed algorithms were also applied in a field study. In this paper data from one subject are considered. The aim of this research work is to provide an effective sensor-based methodology for the evaluation of gait parameters in naturalistic settings.

Keywords: Gait analysis | Hike events | Pressure insoles | Temporal parameters | Wearables

Abstract: The coexistence of autonomous vehicles and humans in public traffic represents a challenging space sharing conflict. In fact, coordination in road traffic is usually made possible by the capacity of humans to communicate their intentions to the fellow road users. With the advent of autonomous vehicles, new ways to communicate intentions should be investigated. To increase the safety of cyclists in mixed traffic is important to early recognize their intentions. This work aims to lay the foundations for augmenting bicycle frames and cycling garments with sensors. To this end, a custom wearable sensor network comprised of three inertial measurement units and a pedaling cadence sensor is proposed and used in a field study with eight participants. The aim of this pilot study is to early-identify the intention of cyclists to perform a left turn, that in right-handed traffic, represents a potentially dangerous maneuver. Collected data are analyzed and annotated, with the support of video, to classify the actions performed by a cyclist when approaching a left turn. Three events where identified: (i) turning of the head, (ii) interruption of pedaling, (iii) left hand out. Results show how the proposed methodology represents a feasible solution to early-identify the intention of a cyclist to turn left.

Keywords: Cycling | Mixed Traffic | Prediction | Safety | Wearable Sensors

Abstract: In this work, we propose a framework that can be used for virtual evaluation of Body-Attached Sensor Networks. Normally, the evaluation of Body-Attached Sensor Networks requires numerous subject tests under laboratory conditions. However, since it is difficult to perform the same motion repeatedly without minute deviations, numerous replicate measurements are required to obtain statistically meaningful measurements. To overcome this limitation, we propose the use of virtual environments. These provide both a high degree of flexibility, since a movement can be repeated in the same way each time, and the ability to test many different sensor setups quickly and with little effort. To this end, we modeled the human body parts of interest using the MATLAB tool Simscape Multibody. Digital twins were then implemented in this model to represent real sensors along with their sensor properties at arbitrary locations. This makes it possible to check many different sensor types and their position on the body in a short time without having to perform subject tests. This framework creates a solid basis for the development of effective Body-Attached Sensor Networks.

Keywords: Digital twins | Multibody simulation | Sensor networks | Virtual environment | Wearable

Abstract: The conventional camera-based systems and electronic gloves for gesture recognition are limited by the influence of lighting conditions, occlusions, and movement restrictions. A wearable smart band with integrated nanocomposite pressure sensors has been developed to overcome these shortcomings. The sensors consist of homogeneously dispersed carbon nanotubes in a polydimethylsiloxane polymer matrix prepared by an optimized synthesis process. The sensor band can actively monitor contractions/relaxations of muscles in the arm due to the sensor's high sensitivity in the low forces and stability. The band has eight sensors placed on a stretchable adhesive textile material and connected to a data logger with a multiplexed sensor interface and wireless communication capabilities. The novel smart band was validated by measurements on ten subjects to perform numerical gestures in American sign language from 0 to 9 with ten trials each. The data were recorded at 100 Hz, and a total of 100 datasets were generated for each subject. By feeding the datasets to an extreme machine learning algorithm that selects features, weights, and biases to classify the gestures, an overall gesture recognition accuracy of 93% could be achieved.

Keywords: American sign language | gesture recognition | polymer carbon nanocomposite (PCN) pressure sensors | Sensor applications | wearable smart band

Abstract: This research activity aims to develop new cycling gloves. A first step was focused on the definition of the functional requirements through user centred design methods. Since vibrations coming to the hand-arm system of a cyclist have a considerable effect a second step was concentrated on the analysis of hand-arm vibrations in road cycling. The paper shows results of laboratory tests executed for three different hand sizes, three different frequency ranges, with two different type of gloves and without gloves. Load conditions used for the test were determined with a former field test. Results obtained were analysed using Analysis of Variance (ANOVA), that showed no significant effect of existing gloves in reducing vibration transmissibility. This led to the need of new kind of cycling gloves that could reduce those vibrations and increase the cyclist’s comfort.

Keywords: Bioengineering | Cycling gloves | Design of experiments | Road cycling | Sport equipment | User centred design | Vibration transmission